Renowned Mathematician Joel David Hamkins Declares AI Models Useless for Solving Math
jelizondo writes:
The Economic Times published an hilarious article about a mathematician opinion of AI for solving math problems:
Renowned mathematician Joel David Hamkins has expressed strong doubts about large language models' utility in mathematical research, calling their outputs "garbage" and "mathematically incorrect". Joel Hamkins, a prominent mathematician and professor of logic at the University of Notre Dame, recently shared his unvarnished assessment of large language models in mathematical research during an appearance on the Lex Fridman podcast. Calling large language models fundamentally useless, he said they give "garbage answers that are not mathematically correct", reports TOI.
Joel David Hamkins is a mathematician and philosopher who undertakes research on the mathematics and philosophy of the infinite. He earned his PhD in mathematics from the University of California at Berkeley and comes to Notre Dame from the University of Oxford, where he was Professor of Logic in the Faculty of Philosophy and the Sir Peter Strawson Fellow of Philosophy at University College, Oxford. Prior to that, he held longstanding positions in mathematics, philosophy, and computer science at the City University of New York.
"I guess I would draw a distinction between what we have currently and what might come in future years," Hamkins began, acknowledging the possibility of future progress. "I've played around with it and I've tried experimenting, but I haven't found it helpful at all. Basically zero. It's not helpful to me. And I've used various systems and so on, the paid models and so on."
Firing a salvo, Joel David Hamkins expressed his frustration with the current AI systems despite experimenting with various models. "I've played around with it and I've tried experimenting, but I haven't found it helpful at all," he stated bluntly.
According to mathematician John Hamkins, AI's tendency to be confidently wrong mirrors some of the most frustrating human interactions. And what is even more concerning for him is how AI systems respond when those errors are highlighted, and not the occasional mathematical error. When Joel David Hamkins highlights clear flaws in their reasoning, the models often reply with breezy reassurances such as, "Oh, it's totally fine." Such AI responses combined with combination of confidence, incorrectness, and resistance to correction puts a threat to collaborative trust that is very much needed for meaningful and essential mathematical dialogue.
"If I were having such an experience with a person, I would simply refuse to talk to that person again," Hamkins said, noting that the AI's behaviour resembles unproductive human interactions he would actively avoid. He believes when it comes to genuine mathematical reasoning, today's AI systems remain unreliable.
Despite these issues, Hamkins recognizes that current limitations may not be permanent. "One has to overlook these kind of flaws and so I tend to be a kind of skeptic about the value of the current AI systems. As far as mathematical reasoning is concerned, it seems not reliable."
His criticism comes amid mixed reactions within the mathematical community about AI's growing role in research. While some scholars report progress using AI to explore problems from the Erds collection, others have urged to exercise caution. Mathematician Terence Tao, for example, has warned that AI can generate proofs that appear flawless but contain subtle errors no human referee would accept. At the heart of the debate is a persistent gap: strong performance on benchmarks and standardized tests does not necessarily translate into real-world usefulness for domain experts.
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